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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import ast\n",
"from pymilvus import MilvusClient\n",
"from sklearn.metrics.pairwise import cosine_similarity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 假设 MilvusClient、DataType、FieldSchema、CollectionSchema 已经正确导入\n",
"# 例如：\n",
"# from your_milvus_module import MilvusClient, DataType, FieldSchema, CollectionSchema\n",
"\n",
"\n",
"# ----------------------------\n",
"# 1. 从 CSV 中读取预计算的评分相似度数据（items_similar.csv）\n",
"def compute_rating_similarity(csv_path):\n",
" \"\"\"\n",
" 从 csv 文件中读取预计算的商品评分相似度数据.\n",
" CSV 文件应包含两列：\n",
" asin, top50_similar\n",
" 其中 top50_similar 是字符串形式的列表，每个元素为 (asin, sim_score)。\n",
" 返回：\n",
" rating_sim: dict, 格式为 {asin: {asin2: sim_score, ...} }\n",
" \"\"\"\n",
" df = pd.read_csv(csv_path)\n",
" rating_sim = {}\n",
" for _, row in df.iterrows():\n",
" asin = row[\"asin\"]\n",
" top50_similar_str = row[\"top50_similar\"]\n",
" try:\n",
" top50_similar_list = ast.literal_eval(top50_similar_str)\n",
" except Exception as e:\n",
" top50_similar_list = []\n",
" # 将列表转换为字典形式\n",
" rating_sim[asin] = {item[0]: item[1] for item in top50_similar_list}\n",
" return rating_sim\n",
"\n",
"\n",
"# ----------------------------\n",
"# 2. Milvus 查询模块：图片和描述相似度\n",
"def query_image_similarity(client, asin, top_k=50):\n",
" \"\"\"\n",
" 查询指定 asin 对应的图片向量，并在 Milvus 中搜索相似商品（图片相似度）。\n",
"\n",
" 返回：\n",
" 字典格式 {asin: sim_score}\n",
" 其中 sim_score 采用 COSINE 指标，计算方式： sim_score = 1 - hit.distance\n",
" \"\"\"\n",
" query_expr = f\"asin == '{asin}'\"\n",
" query_res = client.query(\n",
" collection_name=\"image_embeddings\",\n",
" filter=query_expr,\n",
" output_fields=[\"embedding\"],\n",
" )\n",
" if not query_res:\n",
" return {}\n",
"\n",
" target_embedding = query_res[0][\"embedding\"]\n",
" search_params = {\"metric_type\": \"COSINE\", \"params\": {\"nprobe\": 10}}\n",
" search_results = client.search(\n",
" collection_name=\"image_embeddings\",\n",
" data=[target_embedding],\n",
" anns_field=\"embedding\",\n",
" search_params=search_params,\n",
" limit=top_k,\n",
" filter=f\"asin != '{asin}'\", # 排除自身\n",
" )\n",
"\n",
" sim_dict = {}\n",
" for hit in search_results[0]:\n",
" sim_asin = hit[\"id\"]\n",
" sim_score = 1 - hit[\"distance\"]\n",
" sim_dict[sim_asin] = sim_score\n",
" return sim_dict\n",
"\n",
"\n",
"def query_desc_similarity(client, asin, top_k=50):\n",
" \"\"\"\n",
" 查询指定 asin 对应的描述向量，并在 Milvus 中搜索相似商品（描述相似度）。\n",
"\n",
" 使用您提供的描述向量 schema，假设集合名称为 \"metadata_embeddings\"。\n",
" 返回：\n",
" 字典格式 {asin: sim_score}，sim_score = 1 - hit.distance\n",
" \"\"\"\n",
" query_expr = f\"asin == '{asin}'\"\n",
" query_res = client.query(\n",
" collection_name=\"metadata_embeddings\",\n",
" filter=query_expr,\n",
" output_fields=[\"embedding\"],\n",
" )\n",
" if not query_res:\n",
" return {}\n",
"\n",
" target_embedding = query_res[0][\"embedding\"]\n",
" search_params = {\"metric_type\": \"COSINE\", \"params\": {\"nprobe\": 10}}\n",
" search_results = client.search(\n",
" collection_name=\"metadata_embeddings\",\n",
" data=[target_embedding],\n",
" anns_field=\"embedding\",\n",
" search_params=search_params,\n",
" limit=top_k,\n",
" filter=f\"asin != '{asin}'\", # 排除自身\n",
" )\n",
"\n",
" sim_dict = {}\n",
" for hit in search_results[0]:\n",
" sim_asin = hit[\"id\"]\n",
" sim_score = 1 - hit[\"distance\"]\n",
" sim_dict[sim_asin] = sim_score\n",
" return sim_dict\n",
"\n",
"\n",
"def query_milvus_similarity(client, asin, similarity_type=\"image\", top_k=50):\n",
" \"\"\"\n",
" 根据 similarity_type 参数调用不同的 Milvus 查询：\n",
" - \"image\"：基于图片的查询\n",
" - \"description\"：基于描述的查询\n",
" \"\"\"\n",
" if similarity_type == \"image\":\n",
" return query_image_similarity(client, asin, top_k)\n",
" elif similarity_type == \"description\":\n",
" return query_desc_similarity(client, asin, top_k)\n",
" else:\n",
" return {}\n",
"\n",
"\n",
"# ----------------------------\n",
"# 3. 混合相似度计算（利用预读取评分相似度及 Milvus 查询结果）\n",
"def get_hybrid_similarity(\n",
" asin1, asin2, rating_sim_dict, weights, client, milvus_cache=None\n",
"):\n",
" \"\"\"\n",
" 计算 asin1 与 asin2 之间的混合相似度。\n",
"\n",
" 参数：\n",
" asin1, asin2: 商品标识符\n",
" rating_sim_dict: 从 CSV 中读取的评分相似度字典\n",
" weights: 各部分的权重字典，例如 {\"rating\": 0.6, \"image\": 0.2, \"description\": 0.2}\n",
" client: Milvus 客户端对象\n",
" milvus_cache: 缓存字典，用以减少重复查询\n",
"\n",
" 返回：\n",
" 混合相似度分值\n",
" \"\"\"\n",
" # ① 获取评分相似度：从预计算字典中查找\n",
" rating_score = 0\n",
" if asin1 in rating_sim_dict:\n",
" rating_score = rating_sim_dict[asin1].get(asin2, 0)\n",
"\n",
" if milvus_cache is None:\n",
" milvus_cache = {}\n",
"\n",
" # ② 图片相似度\n",
" if (asin1, \"image\") in milvus_cache:\n",
" image_sim_dict = milvus_cache[(asin1, \"image\")]\n",
" else:\n",
" image_sim_dict = query_milvus_similarity(\n",
" client, asin1, similarity_type=\"image\", top_k=50\n",
" )\n",
" milvus_cache[(asin1, \"image\")] = image_sim_dict\n",
" image_score = image_sim_dict.get(asin2, 0)\n",
" if image_score != 0:\n",
" print(f\"Image similarity for {asin1} and {asin2}: {image_score}\")\n",
"\n",
" # ③ 描述相似度\n",
" if (asin1, \"description\") in milvus_cache:\n",
" desc_sim_dict = milvus_cache[(asin1, \"description\")]\n",
" else:\n",
" desc_sim_dict = query_milvus_similarity(\n",
" client, asin1, similarity_type=\"description\", top_k=50\n",
" )\n",
" milvus_cache[(asin1, \"description\")] = desc_sim_dict\n",
" desc_score = desc_sim_dict.get(asin2, 0)\n",
" if desc_score != 0:\n",
" print(f\"Description similarity for {asin1} and {asin2}: {desc_score}\")\n",
" \n",
"\n",
" # 融合三部分得分\n",
" hybrid_score = (\n",
" weights.get(\"rating\", 0) * rating_score\n",
" + weights.get(\"image\", 0) * image_score\n",
" + weights.get(\"description\", 0) * desc_score\n",
" )\n",
" return hybrid_score\n",
"\n",
"\n",
"# ----------------------------\n",
"# 4. 推荐函数：为用户生成个性化推荐\n",
"def recommend_for_user(\n",
" user_id,\n",
" user_rating_df,\n",
" rating_sim_dict,\n",
" weights,\n",
" client,\n",
" milvus_cache=None,\n",
" top_n=10,\n",
"):\n",
" \"\"\"\n",
" 根据用户的历史评分和混合相似度为用户生成推荐。\n",
"\n",
" 参数：\n",
" user_id: 用户标识\n",
" user_rating_df: 包含 user_id, asin, rating 等信息的 DataFrame\n",
" rating_sim_dict: 预计算的评分相似度字典（从 CSV 读取）\n",
" weights: 各模块混合相似度的权重\n",
" client: Milvus 客户端对象\n",
" milvus_cache: 缓存字典（可选）\n",
" top_n: 返回推荐的商品数量\n",
"\n",
" 返回：\n",
" 推荐列表，每个元素为 (asin, score)\n",
" \"\"\"\n",
" # 找出该用户已评分的商品\n",
" rated_items = set(user_rating_df[user_rating_df[\"user_id\"] == user_id][\"asin\"])\n",
" # 候选商品：在评分相似度字典中出现，但用户未评分的\n",
" candidate_items = set(rating_sim_dict.keys()) - rated_items\n",
"\n",
" scores = {}\n",
" for candidate in candidate_items:\n",
" total_score = 0\n",
" count = 0\n",
" for rated in rated_items:\n",
" sim = get_hybrid_similarity(\n",
" rated, candidate, rating_sim_dict, weights, client, milvus_cache\n",
" )\n",
" total_score += sim\n",
" count += 1\n",
" avg_score = total_score / count if count > 0 else 0\n",
" scores[candidate] = avg_score\n",
"\n",
" # 按照得分排序，返回 Top-N 推荐\n",
" recommended = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:top_n]\n",
" return recommended\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# ① 加载用户评分数据，CSV 文件中应至少包含 'user_id' 和 'asin' 字段\n",
"user_rating_df = pd.read_csv(\"ratings.csv\")\n",
"\n",
"# ② 从预计算的 csv 文件中读取评分相似度数据（items_similar.csv）\n",
"rating_sim_dict = compute_rating_similarity(\"items_similar.csv\")\n",
"\n",
"# ③ 配置混合相似度的权重\n",
"weights = {\"rating\": 0.6, \"image\": 0.2, \"description\": 0.2}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# ④ 初始化 Milvus 客户端\n",
"client = MilvusClient(uri=\"./Amazon_electronics.db\")\n",
"\n",
"# ⑤ 初始化 Milvus 查询缓存字典\n",
"milvus_cache = {}\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image similarity for B0055Q2VS8 and B00FDQQIJS: 0.08797657489776611\n",
"Description similarity for B00171MWSO and B00SMQT014: 0.25822311639785767\n",
"Description similarity for B0055Q2VS8 and B01A9DT8UU: 0.1503007411956787\n",
"Image similarity for B005XU7OL0 and B00STOPZ18: 0.25617748498916626\n",
"Image similarity for B00171MWSO and B013JRQRE8: 0.2458140254020691\n",
"Description similarity for B00DY1R6PO and B00VER8E6A: 0.454379141330719\n",
"Description similarity for B0055Q2VS8 and B01AT0WWI8: 0.12434381246566772\n",
"Image similarity for B00171MWSO and B000EF1JEC: 0.263527512550354\n",
"Description similarity for B005XU7OL0 and B000I3DTJS: 0.3128337860107422\n",
"Image similarity for B008FKDK2M and B00PHGM3I4: 0.15652996301651\n",
"Description similarity for B00171MWSO and B00004WJO4: 0.3122591972351074\n",
"Description similarity for B007DJTBVK and B01A50RU9S: 0.25354552268981934\n",
"Description similarity for B005XU7OL0 and B005J014VK: 0.3145676851272583\n",
"Description similarity for B007DJTBVK and B01C5447XE: 0.2537350654602051\n",
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"Image similarity for B007DJTBVK and B000WXJ5KQ: 0.3704742193222046\n",
"Image similarity for B005XU7OL0 and B011ZCWI9W: 0.2788119912147522\n",
"Description similarity for B008FKDK2M and B00K6JPPVY: 0.32407814264297485\n",
"Image similarity for B00171MWSO and B017LUBO56: 0.27621376514434814\n",
"Description similarity for B00171MWSO and B004M8WF90: 0.24394094944000244\n",
"Image similarity for B008FKDK2M and B016ANCJYA: 0.16513365507125854\n",
"Image similarity for B008FKDK2M and B013QR1JJY: 0.15907502174377441\n",
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"Image similarity for B00DY1R6PO and B00XYFK076: 0.25941169261932373\n",
"Image similarity for B007DJTBVK and B001C4OTDM: 0.3704742193222046\n",
"Description similarity for B007DJTBVK and B010EGV2YM: 0.24348855018615723\n",
"Image similarity for B0055Q2VS8 and B00834SJNA: 0.07206374406814575\n",
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"Image similarity for B00DY1R6PO and B00DJKCWDQ: 0.2873784303665161\n",
"Image similarity for B008FKDK2M and B01HJAHT02: 0.15624171495437622\n",
"Description similarity for B00171MWSO and B00MUM4CZU: 0.2753235101699829\n",
"Description similarity for B005XU7OL0 and B00GR4M7GI: 0.32069987058639526\n",
"Image similarity for B008FKDK2M and B01G516D9O: 0.16154074668884277\n",
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"Image similarity for B0055Q2VS8 and B008VL43B2: 0.08758777379989624\n",
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"Description similarity for B007DJTBVK and B0055IX824: 0.24576199054718018\n",
"Image similarity for B007DJTBVK and B0019D5CZK: 0.348324716091156\n",
"Image similarity for B005XU7OL0 and B00SM8SEW8: 0.28543585538864136\n",
"Image similarity for B00DY1R6PO and B012UFXCUM: 0.2880227565765381\n",
"Image similarity for B00DY1R6PO and B005NI5F18: 0.2862279415130615\n",
"Image similarity for B00171MWSO and B005ER3SDA: 0.28288161754608154\n",
"Image similarity for B005XU7OL0 and B014OSULF2: 0.28096461296081543\n",
"Description similarity for B008FKDK2M and B0090B591K: 0.3238760232925415\n",
"Description similarity for B005XU7OL0 and B007IQNAOM: 0.30597561597824097\n",
"Description similarity for B008FKDK2M and B00L6TNQJ6: 0.3021869659423828\n",
"Description similarity for B00DY1R6PO and B00J0U8RG0: 0.22713983058929443\n",
"Image similarity for B007DJTBVK and B001CI1FP8: 0.3704742193222046\n",
"Image similarity for B008FKDK2M and B00OAYPDZE: 0.16428232192993164\n",
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"Image similarity for B0055Q2VS8 and B000WA0080: 0.059654414653778076\n",
"Description similarity for B00171MWSO and B00138ZKV2: 0.30016201734542847\n",
"Image similarity for B00171MWSO and B00N8PT2WQ: 0.24594545364379883\n",
"Description similarity for B00171MWSO and B000BOWUG2: 0.30024003982543945\n",
"Image similarity for B008FKDK2M and B00KN9YV0I: 0.16014641523361206\n",
"Image similarity for B00171MWSO and B0091PS7TG: 0.27813541889190674\n",
"Image similarity for B0055Q2VS8 and B006ICO0VE: 0.08095306158065796\n",
"Description similarity for B007DJTBVK and B00BD9JR6K: 0.24962258338928223\n",
"Image similarity for B007DJTBVK and B001HKPF20: 0.36890774965286255\n",
"Description similarity for B005XU7OL0 and B00GMAYN6O: 0.3227913975715637\n",
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"Image similarity for B008FKDK2M and B00IS6A6VG: 0.15651369094848633\n",
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"Image similarity for B008FKDK2M and B00KQRNTLY: 0.16014641523361206\n",
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"Description similarity for B00171MWSO and B00EVEILXO: 0.2638061046600342\n",
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"Image similarity for B005XU7OL0 and B00ULZ0NYW: 0.2884257435798645\n",
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"Description similarity for B0055Q2VS8 and B0000DC6B0: 0.19495362043380737\n",
"Image similarity for B00DY1R6PO and B00JRV637C: 0.27955758571624756\n",
"Image similarity for B005XU7OL0 and B01A90CH4M: 0.2592434883117676\n",
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"Description similarity for B0055Q2VS8 and B0009Q1MM0: 0.18348127603530884\n",
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"Image similarity for B0055Q2VS8 and B019ZTZV5A: 0.06376075744628906\n",
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"Description similarity for B007DJTBVK and B005K4700O: 0.248518705368042\n",
"Description similarity for B005XU7OL0 and B005WXL8LK: 0.2986530065536499\n",
"为用户 A0001528BGUBOEVR6T5U 推荐的商品列表：\n",
"ASIN: B00NW32424，得分: 0.1804\n",
"ASIN: B00022OCDC，得分: 0.1796\n",
"ASIN: B00KGXFVVY，得分: 0.1792\n",
"ASIN: B000F277QS，得分: 0.1787\n",
"ASIN: B00SED9BSM，得分: 0.1696\n",
"ASIN: B005Q2NX2O，得分: 0.1634\n",
"ASIN: B005OM0URW，得分: 0.1631\n",
"ASIN: B0051XHY14，得分: 0.1576\n",
"ASIN: B000EZUDQ2，得分: 0.1575\n",
"ASIN: B00M0DTI5I，得分: 0.0954\n"
]
}
],
"source": [
"# ⑥ 指定目标用户（替换为实际用户ID）\n",
"target_user = \"A0001528BGUBOEVR6T5U\"\n",
"recommendations = recommend_for_user(\n",
" target_user,\n",
" user_rating_df,\n",
" rating_sim_dict,\n",
" weights,\n",
" client,\n",
" milvus_cache,\n",
" top_n=10,\n",
")\n",
"\n",
"print(f\"为用户 {target_user} 推荐的商品列表：\")\n",
"for asin, score in recommendations:\n",
" print(f\"ASIN: {asin}，得分: {score:.4f}\")"
]
}
],
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